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yifeih
128 Stars 53 Forks 52 Commits 1 Opened issues

Description

Spring 2014. Stanford CS346 project. A mini relational database with query optimization

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redbase

A mini relational databased for the CS346 Stanford class. See the website for more details: http://web.stanford.edu/class/cs346/project.html

To compile, type "make".

To create a database: ./dbcreate "dbname"

To use the database created: ./redbase "dbname"

To destroy the database: ./dbdestroy "dbname"

My implementation documentation for each layer can be found in the "doc" folder.

Here is a VERY high level overview for the "parts" of the system: RM - Record Manager The Record manager organizes pages into databases. It keeps a header page with informaiton about the record sizes, the number of pages in the table, etc. Each page mantains information about how the records are stored on the page. There is also an iterator for scanning through the records of the table

IX - Index Layer This layer holds the index layer. It manages a set of pages that store the nodes of the B+ tree, and implements the logic of insertion, deletion, and search. There is also an iterator for retrieving indices, with or without conditions.

SM - System Management Files involved with this have to do with creating, destroying and manipulating the database. When the appropriate instructions are given by the user to redbase, SM will organize the files for keeping track of databases, tables, indices. It also deals with loading data into databases.

QL - Query Layer This is the most interesting part of the system. It takes the output of the parser (that was given to us) and implements the appropraite SQL query. Details of what is implemented is in http://web.stanford.edu/class/cs346/ql.html. The QL layer constructs the query tree plan, and executes them with iterators. It constructs the tree with objects that I've called "nodes" in the files, and keeps a pointer to the root. Then, when a record is requested from the root, it recursively calls for the next record from the leaves.

The additional component I implemented was a dynamic programming query optimizer. The documentation is in the pdf labeled queryoptimizerDOC.

It is a simplified version of the Selinger Query Optimizer that uses only dynamic programming. It keeps an estimate of the number of records and unique values of each attribute for each table, and uses estimates to determine the order of the join. See queryoptimizerDOC for more details! :)

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